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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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4 results for “POD-Galerkin”

CD-ROM: Complemented Deep-Reduced Order Modelphysics.flu-dyn

A new method uses neural networks to improve POD-Galerkin models for complex systems.

problem Improving computational efficiency and accuracy in solving non-linear high-dimensional systems.

method Deep learning-based closure modeling using neural networks to approximate POD-Galerkin operators.

result The CD-ROM approach produces more accurate and stable models for complex systems.

model order reduction deep learning neural networks POD-Galerkin Navier-Stokes equations

2022-02-22abs ↗pdf ↗

Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparisoneess.SY

Review and compare model order reduction methods for process engineering.

problem Creating computationally efficient yet accurate models for real-time applications.

method Nonlinear model order reduction methods, including general-purpose and tailored approaches for chemical processes.

result Comparison of eight model order reduction methods applied to an air separation process model.

model order reduction process engineering nonlinear dynamics machine learning POD-Galerkin

2025-06-15abs ↗pdf ↗

Reduced-Order Modeling Of Hidden Dynamicsstat.ML

The objective of this paper is to investigate how noisy and incomplete observations can be integrated in the process of building a reduced-order model. This problematic arises in many scientific domains where there exists a need for accurate low-order descriptions of highly-complex phenomena, which can not be directly …

2015-10-08abs ↗pdf ↗

An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusionmath.NA

Paper uses autoencoders for efficient reduced-order modeling of eigenvalue problems.

problem Efficiently modeling eigenvalue problems in high dimensions.

method Autoencoder-based reduced-order modeling for eigenvalue problems.

result Autoencoder-based models outperform standard POD-Galerkin methods in neutron diffusion applications.

autoencoder reduced-order modeling eigenvalue problems neutron diffusion nuclear reactor physics

2020-08-15abs ↗pdf ↗